R
e
g
u
l
a
r
Iss
ue
-
1
37 -
Please
c
i
t
e
t
h
is
ar
t
i
c
le
as
:
A
.
L
.
S
uárez
-C
e
t
rulo,
D
.
Q
ui
n
t
a
n
a,
A
.
C
er
v
a
n
t
es.
M
a
c
h
i
n
e
L
ear
n
i
n
g f
or
F
i
n
a
n
c
ial
Predi
ct
io
n
U
n
der
R
e
g
ime
C
h
a
n
g
e
U
si
n
g T
e
c
hn
i
c
al
A
n
al
y
sis
:
A Sy
s
t
ema
t
i
c
R
e
v
ie
w
,
I
n
t
er
n
a
t
io
n
al
J
our
n
al
o
f
I
n
t
era
ct
i
v
e
M
ul
t
imedia
a
n
d
A
r
t
i
f
i
c
ial
I
n
t
elli
g
e
n
c
e,
v
ol.
9,
n
o.
1,
pp.
137
-
148,
2024,
h
tt
p
://
d
x
.doi.or
g
/
10.9781
/
i
j
imai.2023.06.003
K
e
y
w
o
rds
C
oncep
t
Dr
if
t
,
Finance
,
M
achine
L
ea
r
ning
,
M
e
t
a
L
ea
r
ning
,
Regime
C
hange
,
S
y
s
t
ema
t
ic
L
i
t
e
r
a
t
u
r
e
Re
v
ie
w.
Abstr
a
c
t
Recen
t
c
r
ises
, r
ecessions
and
bubbles
ha
v
e
s
t
r
essed
t
he
non
-
s
t
a
t
iona
ry
na
t
u
r
e
and
t
he
p
r
esence
of
d
r
as
t
ic
s
t
r
uc
t
u
r
al
changes
in
t
he
financial
domain
. T
he
mos
t
r
ecen
t
li
t
e
r
a
t
u
r
e
sugges
t
s
t
he
use
of
con
v
en
t
ional
machine
lea
r
ning
and
s
t
a
t
is
t
ical
app
r
oaches
in
t
his
con
t
ex
t
. U
nfo
r
t
una
t
el
y,
se
v
e
r
al
of
t
hese
t
echniques
a
r
e
unable
o
r
slo
w
t
o
adap
t
t
o
changes
in
t
he
p
r
ice
-
gene
r
a
t
ion
p
r
ocess
. T
his
s
t
ud
y
aims
t
o
su
rv
e
y
t
he
r
ele
v
an
t
li
t
e
r
a
t
u
r
e
on
M
achine
L
ea
r
ning
fo
r
financial
p
r
edic
t
ion
unde
r r
egime
change
emplo
y
ing
a
s
y
s
t
ema
t
ic
app
r
oach
.
It
r
e
v
ie
w
s
k
e
y
pape
r
s
w
i
t
h
a
special
emphasis
on
t
echnical
anal
y
sis
. T
he
s
t
ud
y
discusses
t
he
g
r
o
w
ing
numbe
r
of
con
t
r
ibu
t
ions
t
ha
t
a
r
e
b
r
idging
t
he
gap
be
t
w
een
t
w
o
sepa
r
a
t
e
communi
t
ies
,
one
focused
on
da
t
a
s
t
r
eam
lea
r
ning
and
t
he
o
t
he
r
on
economic
r
esea
r
ch
. H
o
w
e
v
e
r,
i
t
also
ma
k
es
appa
r
en
t
t
ha
t
w
e
a
r
e
s
t
ill
in
an
ea
r
l
y
s
t
age
.
T
he
r
ange
of
machine
lea
r
ning
algo
r
i
t
hms
t
ha
t
ha
v
e
been
t
es
t
ed
in
t
his
domain
is
v
e
ry w
ide
,
bu
t
t
he
r
esul
t
s
of
t
he
s
t
ud
y
do
no
t
sugges
t
t
ha
t
cu
rr
en
t
l
y
t
he
r
e
is
a
specific
t
echnique
t
ha
t
is
clea
r
l
y
dominan
t
.
D
O
I:
1
0
.
9
7
8
1
/
i
j
i
m
a
i
.
2
0
2
3.
0
6
.
00
3
M
achine
L
ea
r
ning
fo
r
Financial
P
r
edic
t
ion
U
nde
r
Regime
C
hange
U
sing
T
echnical
A
nal
y
sis
: A
S
y
s
t
ema
t
ic
Re
v
ie
w
A
nd
r
és
L.
Suá
r
ez
-C
e
t
r
ulo
1
, D
a
v
id
Quin
t
ana
2
, A
lejand
r
o
C
e
rv
an
t
es
3
*
1
I
r
eland’s
C
en
t
r
e
fo
r A
pplied
A
I
(
C
e
ADA
R)
, U
ni
v
e
r
si
t
y C
olle
g
e
D
ublin
(
I
r
eland)
2
D
epa
r
t
men
t
of
C
ompu
t
e
r
Science
and
E
n
g
inee
r
in
g, U
ni
v
e
r
sidad
C
a
r
los
III
de
M
ad
r
id
, Av
da
. U
ni
v
e
r
sidad
30
,
28911
L
e
g
anes
(Spain)
3
E
scuela
Supe
r
io
r
de
I
n
g
enie
r
ía
y T
ecnolo
g
ía
, U
ni
v
e
r
sidad
I
n
t
e
r
nacional
de
L
a
Rioja
(
UN
I
R)
, L
o
gr
oño
(Spain)
* C
o
rr
espondin
g
au
t
ho
r:
alejand
r
o
.
ce
rv
an
t
es
r
o
v
i
r
a
@
uni
r.
ne
t
R
e
c
e
i
v
e
d
2
6
M
ay
2
0
22
| Acc
e
p
te
d
2
8
Apr
i
l
2
0
2
3 | Pub
l
i
sh
e
d
2
3
J
un
e
2
0
2
3
I
.
I
ntroduction
F
in
a
nci
a
l
ma
rk
e
t
s
can
be
desc
r
ibed
as
an
e
v
olu
t
iona
ry
and
nonlinea
r
d
y
namical
complex
s
y
s
t
em
[
1
]
,
[
2
]
.
Fo
r
ecas
t
ing
in
t
he
financial
domain
has
t
r
adi
t
ionall
y
been
pe
r
fo
r
med
unde
r
t
he
assump
t
ion
t
ha
t
t
he
unde
r
l
y
ing
da
t
a
has
been
c
r
ea
t
ed
b
y
a
linea
r
p
r
ocess
[
3
]
. A
no
t
he
r
line
of
w
o
rk
t
o
ma
k
e
financial
p
r
edic
t
ions
is
t
o
use
machine
lea
r
ning
(
ML
)
. T
hese
algo
r
i
t
hms
ha
v
e
su
r
p
r
ised
financial
expe
r
t
s
[
4
]
–
[
6
]
because
of
t
hei
r
success
in
mapping
nonlinea
r r
ela
t
ionships
w
i
t
hou
t
p
r
io
r k
no
w
ledge
[
7
]
. D
eep
lea
r
ning
algo
r
i
t
hms
(neu
r
al
ne
t
w
o
rk
s)
and
ensembles
ha
v
e
been
some
of
t
he
t
echniques
ob
t
aining
t
he
bes
t
r
esul
t
s
fo
r
s
t
oc
k
t
r
end
p
r
edic
t
ion
[
8
]
–
[
13
]
.
D
iffe
r
en
t
c
r
ises
, r
ecessions
and
bubbles
,
such
as
t
he
C
OV
I
D-
19
pandemic
,
o
r v
ola
t
ile
mid
-
t
e
r
m
t
r
ends
in
c
ry
p
t
o
ma
rk
e
t
s
,
ha
v
e
made
appa
r
en
t
t
he
non
-
s
t
a
t
iona
ry
na
t
u
r
e
and
t
he
p
r
esence
of
d
r
as
t
ic
s
t
r
uc
t
u
r
al
changes
in
financial
ma
rk
e
t
s
[
14
]
. D
u
r
ing
t
hese
pe
r
iods
,
mean
r
e
t
u
r
ns
, v
ola
t
ili
t
y
and
co
rr
ela
t
ions
among
asse
t
s
t
end
t
o
change
quic
k
l
y
[
15
]
. T
his
has
b
r
ough
t
a
tt
en
t
ion
t
o
t
he
p
r
oblem
of
concep
t
d
r
if
t
[
16
]
in
compu
t
a
t
ional
finance
[
17
]
. M
an
y r
ecen
t
r
esea
r
ch
w
o
rk
s
poin
t
ou
t
t
ha
t
financial
asse
t
s
o
r
companies
p
r
esen
t
diffe
r
en
t
s
t
a
t
es
t
ha
t
ma
y
r
epea
t
o
r
no
t
o
v
e
r
t
ime
o
r
e
v
ol
v
e
due
t
o
infla
t
ion
,
defla
t
ion
,
o
r
changes
in
suppl
y
and
demand
[
18
]
–
[
24
]
.
I
n
finance
,
a
change
in
t
he
collec
t
i
v
e
beha
v
iou
r
of
ma
rk
e
t
pa
r
t
icipan
t
s
and
t
hei
r r
eac
t
ions
is
called
a
r
egime
change
(R
C
)
. A
s
co
v
e
r
ed
b
y
t
he
ma
rk
ed
efficienc
y
h
y
po
t
hesis
[
25
]
, w
e
canno
t
obse
rv
e
t
he
indi
v
idual
beha
v
iou
r
of
a
t
r
ade
r
o
r
i
t
s
in
t
en
t
ions
.
I
ns
t
ead
, w
e
can
onl
y
obse
rv
e
changes
in
t
he
p
r
ice
d
y
namics
and
mac
r
o
o
r
mic
r
o
-
economic
v
a
r
iables
and
ex
t
r
apola
t
e
t
he
changes
t
ha
t
ma
k
e
t
hem
modif
y
t
hei
r
beha
v
iou
r. T
he
execu
t
ion
of
t
hese
s
t
r
a
t
egies
is
t
he
ac
t
ual
gene
r
a
t
i
v
e
p
r
ocess
of
t
he
obse
rv
ed
t
ime
se
r
ies
of
p
r
ices
o
r
t
r
ends
. T
he
es
t
ima
t
ion
of
t
he
hidden
p
r
ocesses
d
r
i
v
ing
t
he
ma
rk
e
t
in
t
o
diffe
r
en
t
r
egimes
is
of
t
en
app
r
oached
using
r
egime
-
s
w
i
t
ching
models
,
a
t
y
pe
of
t
ime
se
r
ies
model
w
he
r
e
pa
r
ame
t
e
r
s
can
ha
v
e
diffe
r
en
t
v
alues
in
diffe
r
en
t
c
y
cles
[
26
]
.
D
espi
t
e
t
he
fac
t
t
ha
t
a
r
t
ificial
in
t
elligence
has
r
ecen
t
l
y
become
a
t
r
end
and
e
v
en
a
buzz
w
o
r
d
in
man
y
indus
t
r
ies
,
t
his
has
no
t
become
t
he
main
t
r
end
y
e
t
fo
r
t
r
ading
s
y
s
t
ems
. T
his
is
mainl
y
due
t
o
t
he
high
complexi
t
y
and
ha
r
d
explainabili
t
y
of
t
hese
models
[
27
]
,
being
t
he
second
a
mus
t
fo
r
s
t
a
k
eholde
r
s
and
decision
-
ma
k
e
r
s
in
t
his
sec
t
o
r
[
28
]
.
I
ns
t
ead
,
t
r
ade
r
s
t
end
t
o
iden
t
if
y
di
r
ec
t
ional
changes
in
t
he
ma
rk
e
t
s
t
a
t
e
using
diffe
r
en
t
popula
r
indica
t
o
r
s
t
ailo
r
ed
acco
r
ding
t
o
t
hei
r
needs
.
Tr
adi
t
ionall
y,
t
he
li
t
e
r
a
t
u
r
e
has
used
s
t
a
t
ic
me
t
hods
t
o
in
t
e
r
p
r
e
t
pa
tt
e
r
ns
based
on
t
he
meaning
of
t
hese
indica
t
o
r
s
and
t
hei
r
his
t
o
r
ical
co
rr
ela
t
ion
t
o
fu
t
u
r
e
p
r
ices
. H
o
w
e
v
e
r,
t
his
co
rr
ela
t
ion
ma
y v
a
ry
o
v
e
r
